© Philip Montgomery for The New York Times
Certain kinds of clouds are misbehaving – appearing more often and lower in the sky than they used to. To help identify the factors influencing these changes (e.g. shifts in Earth’s long-term weather patterns), scientists have asked people around the world with cameras to submit fresh images of these clouds as a part of the NASA-supported Space Cloud Watch project. Now, one volunteer has developed a new tool to help other Space Cloud Watch volunteers work more efficiently.
The misbehaving clouds are “noctilucent” or “night-shining” clouds (NLCs). These clouds scatter light from the Sun long after sunset and long before sunrise, giving them a silvery glow. But despite this glow, it can be hard to differentiate NLCs from lower-altitude look-alikes. That confusion has meant extra work for project leaders.
Volunteer Namai Chandra shared, “I noticed that NLC images were being manually verified by the project leaders. It felt like a task well-suited for a human-in-the-loop machine learning pipeline, one that could handle the repetitive screening automatically, while keeping human judgment central for the images that matter most.” In other words, Namai found a way to help observers verify when they are indeed seeing NLCs and when they’re not.
Namai reached out to the Space Cloud Watch scientists Drs. Chihoko Cullens and Brentha Thurairajah, who were delighted with his idea. Namai soon developed a machine learning pipeline, training it on a variety of cloud images, including both the NLCs and the lower altitude look-alikes that are often submitted to Space Cloud Watch. The pipeline combines image pre-screening, cloud classification, and confidence-based review routing. After several rounds of development, testing, and refinement, he released his NLC identification tool to the project. This tool is now being used by cloud contributors who are unsure whether they have observed NLCs, as well as project scientists that want to flag images for review.
Grab a camera and join the Space Cloud Watch project today! If you’ve hesitated to contribute to Space Cloud Watch because you were not certain if what you were seeing was a noctilucent cloud, you now have a way to check before you share – thanks to Namai.
5 min read
As humanity looks to the Moon and stars for future exploration, predicting space weather — conditions in space primarily driven by the Sun — is more important than ever.
Now, a team of astrophysicists and data scientists with NASA’s COFFIES (Consequence Of Fields and Flows in the Interior and Exterior of the Sun) has developed a novel machine-learning model capable of predicting the emergence of active regions on the Sun up to 12 hours before they appear.
The Sun is constantly churning. Intense concentrations of localized magnetic fields can suddenly break through the solar surface, forming sunspots. Space weather forecasters then collectively number and track sunspots since they are visible manifestations of active regions, which serve as the main engines behind severe space weather events such as solar flares and coronal mass ejections. These eruptions send waves of high-energy radiation and charged particles across space, creating storms that can threaten astronauts, disable satellites, and disrupt radio communications on Earth.
By bridging expertise across different scientific institutions, COFFIES, a NASA DRIVE (Diversify, Realize, Integrate, Venture, Educate) Science Center, brought together a team of researchers from New Jersey Institute of Technology (NJIT), Princeton University, and NASA’s Ames Research Center in California’s Silicon Valley. The team turned to advanced artificial intelligence architectures — which dictate how data is processed and used to produce reliable predictions or actions — to capture subtle, time-based pattern changes on the solar surface before an active region took shape. By analyzing data captured by the agency’s Solar Dynamics Observatory and using NASA Ames’ supercomputing resources, this new approach, published in the Journal of Geophysical Research: Machine Learning and Computation, looks at fluctuations in acoustic waves caused by sunspot regions when the regions form beneath the solar surface and begin the journey upward to emerge on the surface.
“We cannot directly see the magnetic structure while it is still rising through the solar interior. Instead, we must look for indirect effects — very small changes in the magnetic field and in the pattern of acoustic waves continually traveling through the Sun,” said Alexander Kosovichev, a COFFIES co-investigator at NJIT. “The developed technique identifies precursors associated with an emerging active region in slight changes of the Sun’s acoustic power — more like a slight change in rhythm within a very noisy orchestra.”
To develop current operational forecasts, the National Oceanic and Atmospheric Administration’s Space Weather Prediction Center and the United States Air Force monitor active regions that are already visible on the Sun to analyze the regions’ characteristics and estimate the probability of solar flares.
The COFFIES team aims to revolutionize this process. The AI model the team developed a specialized early detection system to handle very long sequences of data — called sliding-window transformer architecture — to use observations to find tiny reductions in the Sun’s acoustic activity and magnetic field, signals that scientists struggled to capture until now. These reductions form patterns that the AI model uses to predict active regions several hours before they become visible on the solar surface. Instead of looking at all activity on the solar surface at once, like earlier deep learning approaches have done, this new model moves a fixed-size “viewing window” across a long timeline of the Sun’s activity to focus on recent data while remembering overall patterns. This method allows forecasters the ability to predict approximate locations of emerging sunspots, rather than relying on counting already visible sunspots.
This promising AI architecture shows how deep machine learning can contribute to heliophysics — the field studying the nature of the Sun and how it influences the very nature of space and the planets that exist there. While the model is not ready for operational real-time forecasting, the team plans to validate the approach across many more known solar events to fine-tune the model.
NASA’s real-time space weather monitoring
As NASA focuses on sending humans to explore the Moon with the Artemis missions and sending the first crewed missions to Mars, monitoring and forecasting space weather is important for ensuring the safety of our astronauts and the equipment they rely on. This predictive leap from the COFFIES team could prove vital for safeguarding technology and deep-space explorers from the volatile environment of our solar system.
Teams across NASA and NOAA collaborate to transition research capabilities into actual 360-degree space weather monitoring operational tools — including NASA’s Space Radiation Analysis Group, Moon to Mars Space Weather Analysis Office (M2M SWAO), and Community Coordinated Modeling Center as well as NOAA’s Space Weather Prediction Center. Sunspot region emergence prediction capabilities, especially of the Sun’s far side, could provide new information that supplements current models used by these teams.
“The COFFIES AI model is exciting to our team because it could provide us with new capabilities towards predicting potential flaring locations ahead of time,” said Michelangelo Romano, M2M SWAO deputy director. “With this heads up, we can provide additional support to NASA missions.”
NASA’s COFFIES is one of three DRIVE Science Centers created to encourage collaborative science by establishing centers that are made of multidisciplinary teams from several institutions across the U.S. These pioneering facilities employ modelers, theoreticians, computer scientists, and observers to study important mysteries of our star and its influence, a branch of science known as heliophysics.
The COFFIES team focuses on the interconnected processes behind the Sun’s activity. Understanding the Sun’s interior and magnetic variability is key to advancing our understanding of the Sun’s 11-year activity cycle and fine-tuning space weather forecasting tools.
© Manuel Ausloos/Reuters
© Manuel Ausloos/Reuters
© Jaime Molina for The New York Times
4 min read
An aircraft powered by a megawatt-class hybrid-electric engine developed in collaboration with NASA and built by GE Aerospace, demonstrated flight of an innovation that can inform new generations of fuel-saving aircraft power systems.
Mounted to a Saab 340B aircraft, the engine flew at Farnborough International Air Show in the United Kingdom. It was the public debut of a system that has in recent months made historic test flights, becoming the first hybrid electric-powered aircraft to fly above 30,000 feet.
“This achievement reflects what NASA does best in aeronautics: we explore bold possibilities, validate them through rigorous research and testing, and work with industry to turn breakthrough ideas into technologies that bring real value for the American people,” said Laurie Grindle, director of the Aeronautics Division within the agency’s Research and Technology Mission Directorate at NASA Headquarters in Washington.
The testing leveraged work done through NASA’s former Electrified Powertrain Flight Demonstration project and the agency’s ongoing Subsonic Vehicle Technologies and Tools project – years of collaborative research that included key testing at NASA test facilities.
The engine integrates electric motors, a gas turbine, and energy storage capabilities. It was designed to demonstrate the capacity to power an aircraft around the size of a regional-class jet, reducing fuel burn and costs without sacrificing performance. The unit’s technology and designs are expected to be used to help develop future hybrid systems that could lower airline operating costs.
The demonstration flight came after years of rapid development for the technology. For NASA, it also validates work that stretches back to a time when hybrid aviation propulsion seemed almost beyond the horizon of possibility.
LAURIE A. GRINDLE
Director of the Aeronautics Division within the agency's Research and Technology Mission Directorate
“This is the culmination of more than 15 years of work, and we did that because it’s going to have an impact for aircraft that will help reduce energy use and help U.S. companies and the public,” said Ralph Jansen, aerospace engineer at NASA’s Glenn Research Center in Cleveland. “It’s about having a vision that no one believes can happen and then doing the work to define and execute the research and development needed to make it happen.”
This accomplishment was possible because of the collaborative effort of hundreds of people working on Electrified Powertrain Flight Demonstration and Subsonic Vehicle Technologies and Tools projects across NASA centers, in conjunction with GE Aerospace and its partner companies.
In recent years, aviation has seen a boom in small aircraft and drones powered by electrical systems drawing from batteries. But large passenger and cargo planes require complex engines capable of supplying massive amounts of power. So more than a decade ago when NASA began contemplating hybrid systems, just the possibility of using electric motors to supplement some energy was a daunting engineering challenge.
NASA spent about seven years performing preliminary research, working with small businesses and other partners to consider technological obstacles and the potential commercial viability of hybrid systems. During that time, the agency addressed several barriers to implementation including the power, thermal, and battery technology, and the integration of the power system, engine, and aircraft.
Through the agency’s Electrified Powertrain Flight Demonstration award, GE Aerospace and NASA worked with researchers to develop lighter and more efficient power systems and shrink key components – sometimes dramatically.
NASA and GE Aerospace also leveraged agency facilities and resources to further their research. In 2022, GE Aerospace tested an integrated version of its propulsion system at NASA’s Electric Aircraft Testbed at the agency’s Neil A. Armstrong Test Facility in Sandusky, Ohio. Testing allowed the system to operate in conditions simulating 45,000 feet in altitude, the range in which commercial single-aisle aircraft fly.
The team added components, including electric motors, power converters, propellers, and a GE Aerospace commercial engine, followed by more ground tests and eventual flight tests. For the researchers who’d spent years on the concept, seeing the engine powering an aircraft in flight was a major step in a long journey.
“I’ve got to say, I was pretty touched seeing it fly. It was just awesome,” Jansen said. “It’s just like a regular plane, which is probably the best thing of all.”
NASA’s current support for this research is through the Aeronautics Division of its Research and Technology Mission Directorate.
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